Data-driven random forest forecasting method of monthly electricity consumption

2022 ◽  
Author(s):  
Xinfu Pang ◽  
Changfeng Luan ◽  
Li Liu ◽  
Wei Liu ◽  
Yuancheng Zhu
Keyword(s):  
Random Forest ◽  
Electricity Consumption ◽  
Data Driven ◽  
Forecasting Method ◽  
Monthly Electricity Consumption

A data-driven shale gas production forecasting method based on the multi-objective random forest regression

2021 ◽  
Vol 196 ◽  
pp. 107801
Author(s):  
Liang Xue ◽  
Yuetian Liu ◽  
Yifei Xiong ◽  
Yanli Liu ◽  
Xuehui Cui ◽  
...  
Keyword(s):  
Random Forest ◽  
Shale Gas ◽  
Gas Production ◽  
Data Driven ◽  
Random Forest Regression ◽  
Multi Objective ◽  
Production Forecasting ◽  
Forecasting Method

scholarly journals Data-Driven Wildfire Risk Prediction in Northern California

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 109
Author(s):  
Ashima Malik ◽  
Megha Rajam Rao ◽  
Nandini Puppala ◽  
Prathusha Koouri ◽  
Venkata Anil Kumar Thota ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Curves ◽  
Data Driven ◽  
Northern California ◽  
Combined Model ◽  
Wildfire Risk ◽  
Study Results ◽  
Forest Models ◽  
Random Forest Models

Over the years, rampant wildfires have plagued the state of California, creating economic and environmental loss. In 2018, wildfires cost nearly 800 million dollars in economic loss and claimed more than 100 lives in California. Over 1.6 million acres of land has burned and caused large sums of environmental damage. Although, recently, researchers have introduced machine learning models and algorithms in predicting the wildfire risks, these results focused on special perspectives and were restricted to a limited number of data parameters. In this paper, we have proposed two data-driven machine learning approaches based on random forest models to predict the wildfire risk at areas near Monticello and Winters, California. This study demonstrated how the models were developed and applied with comprehensive data parameters such as powerlines, terrain, and vegetation in different perspectives that improved the spatial and temporal accuracy in predicting the risk of wildfire including fire ignition. The combined model uses the spatial and the temporal parameters as a single combined dataset to train and predict the fire risk, whereas the ensemble model was fed separate parameters that were later stacked to work as a single model. Our experiment shows that the combined model produced better results compared to the ensemble of random forest models on separate spatial data in terms of accuracy. The models were validated with Receiver Operating Characteristic (ROC) curves, learning curves, and evaluation metrics such as: accuracy, confusion matrices, and classification report. The study results showed and achieved cutting-edge accuracy of 92% in predicting the wildfire risks, including ignition by utilizing the regional spatial and temporal data along with standard data parameters in Northern California.

scholarly journals Data-driven approach for tailoring facilitation strategies to overcome implementation barriers in community pharmacy

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Lydia Moussa ◽  
Shalom Benrimoj ◽  
Katarzyna Musial ◽  
Simon Kocbek ◽  
Victoria Garcia-Cardenas
Keyword(s):  
Random Forest ◽  
Community Pharmacy ◽  
Implementation Research ◽  
Theoretical Domains Framework ◽  
Professional Services ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Community Pharmacies ◽  
Continuous Change ◽  
Implementation Barriers

Abstract Background Implementation research has delved into barriers to implementing change and interventions for the implementation of innovation in practice. There remains a gap, however, that fails to connect implementation barriers to the most effective implementation strategies and provide a more tailored approach during implementation. This study aimed to explore barriers for the implementation of professional services in community pharmacies and to predict the effectiveness of facilitation strategies to overcome implementation barriers using machine learning techniques. Methods Six change facilitators facilitated a 2-year change programme aimed at implementing professional services across community pharmacies in Australia. A mixed methods approach was used where barriers were identified by change facilitators during the implementation study. Change facilitators trialled and recorded tailored facilitation strategies delivered to overcome identified barriers. Barriers were coded according to implementation factors derived from the Consolidated Framework for Implementation Research and the Theoretical Domains Framework. Tailored facilitation strategies were coded into 16 facilitation categories. To predict the effectiveness of these strategies, data mining with random forest was used to provide the highest level of accuracy. A predictive resolution percentage was established for each implementation strategy in relation to the barriers that were resolved by that particular strategy. Results During the 2-year programme, 1131 barriers and facilitation strategies were recorded by change facilitators. The most frequently identified barriers were a ‘lack of ability to plan for change’, ‘lack of internal supporters for the change’, ‘lack of knowledge and experience’, ‘lack of monitoring and feedback’, ‘lack of individual alignment with the change’, ‘undefined change objectives’, ‘lack of objective feedback’ and ‘lack of time’. The random forest algorithm used was able to provide 96.9% prediction accuracy. The strategy category with the highest predicted resolution rate across the most number of implementation barriers was ‘to empower stakeholders to develop objectives and solve problems’. Conclusions Results from this study have provided a better understanding of implementation barriers in community pharmacy and how data-driven approaches can be used to predict the effectiveness of facilitation strategies to overcome implementation barriers. Tailored facilitation strategies such as these can increase the rate of real-time implementation of innovations in healthcare, leading to an industry that can confidently and efficiently adapt to continuous change.

Monthly Electricity Consumption Forecasting: A Step-Reduction Strategy and Autoencoder Neural Network

2020 ◽  
pp. XX10-XX10
Author(s):  
Zhenghui Li ◽  
Kangping Li ◽  
Fei Wang ◽  
Zhiming Xuan ◽  
Zengqiang Mi ◽  
...  
Keyword(s):  
Neural Network ◽  
Electricity Consumption ◽  
Reduction Strategy ◽  
Monthly Electricity Consumption

scholarly journals A Hybrid Approach: Dynamic Diagnostic Rules for Sensor Systems in Industry 4.0 Generated by Online Hyperparameter Tuned Random Forest

2020 ◽  
Author(s):  
Ahlam Mallak ◽  
Madjid Fathi
Keyword(s):  
Random Forest ◽  
Random Search ◽  
Hybrid Approach ◽  
Fault Detection And Diagnosis ◽  
Data Driven ◽  
Sensor Data ◽  
Sensor Systems ◽  
Hydraulic Test ◽  
Model Based ◽  
Detection And Diagnosis

In this work, A hybrid component Fault Detection and Diagnosis (FDD) approach for industrial sensor systems is established and analyzed, to provide a hybrid schema that combines the advantages and eliminates the drawbacks of both model-based and data-driven methods of diagnosis. Moreover, spotting the light on a new utilization of Random Forest (RF) together with model-based diagnosis, beyond its ordinary data-driven application. RF is trained and hyperparameter tuned using 3-fold cross-validation over a random grid of parameters using random search, to finally generate diagnostic graphs as the dynamic, data-driven part of this system. Followed by translating those graphs into model-based rules in the form of if-else statements, SQL queries or semantic queries such as SPARQL, in order to feed the dynamic rules into a structured model essential for further diagnosis. The RF hyperparameters are consistently updated online using the newly generated sensor data, in order to maintain the dynamicity and accuracy of the generated graphs and rules thereafter. The architecture of the proposed method is demonstrated in a comprehensive manner, as well as the dynamic rules extraction phase is applied using a case study on condition monitoring of a hydraulic test rig using time series multivariate sensor readings.

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